The Healthy Eating Index (HEI) 2015 is a tool developed by the United States Department of Agriculture to evaluate the extent to which an individual’s diet is consistent with the 2015 Dietary Guidelines for Americans.

 

HEI scores may be calculated using variables available in NDSR output files. To facilitate generating HEI-2015 scores we have created SAS programs that allow for calculating HEI-2015 scores. The following guide is available to support you in using the SAS code for calculating HEI-2015 scores.

 

Guide to Creating Variables Needed to Calculate Scores for Each Component of the Healthy Eating Index-2015 (HEI-2015) (pdf)

 

Note: This guide is most relevant to those using NDSR 2015 or a subsequent version of the program. Contact NCC if you are interested in calculating HEI scores using versions prior to NDSR 2015.

NDSR output files include intake estimates for monosaccharides (fructose, galactose, glucose, tagatose), disaccharides (lactose, maltose, sucrose) and a variety of polyols (erythritol, inositol, isomalt, lactitol, maltitol, mannitol, pinitol, sorbitol, xylitol). Intake estimates for oligosaccharides are not available. Thus, intake of all types of FODMAPs except oligosaccharides may be estimated using data available in the output files.

The NCC Food and Nutrient Database includes more foods and nutrients than other research quality U.S. databases (comparison table).

One of the features of NDSR that makes it the choice of researchers is the small number of missing nutrient values for foods in the database. This is an essential feature because missing nutrient values for foods are ultimately calculated as zeros in nutrient intake estimates.

To minimize the number of missing values, NCC uses several standardized procedures to impute or logically calculate an estimation. These procedures are described in Procedures for estimating nutrient values for food composition databases. To summarize, one of the following procedures is employed to estimate a nutrient value for a food when an analytic value is not available from the USDA Nutrient Data Laboratory or cannot be found in the literature:

• Value from a different but similar food is used. (e.g., missing nutrient for wild duck may be judged to be the same as known nutrient in domestic duck)
• Value for another form of the same food is calculated (e.g., convert from raw to cooked values using retention factors)
• Calculate values from other components in the same food (e.g., estimate beta-carotene from Vitamin A)
• Calculate value from household recipes or commercial food product formulations for multicomponent foods (e.g., Manufacturers may provide ingredient listing and macronutrient composition of product. From the macro-nutrients, NCC database scientists estimate the amount of each ingredient. Based on the estimated amounts of the ingredients, micro-nutrients can be imputed for the product.)

Missing values are allowed in the database for foods that are consumed in very small quantities, such as spices, or where there are no data to indicate whether the nutrient exists in the food.

Some nutrients have no missing values, but a high percentage of imputed values. An example is Vitamin A which is calculated from provitamin A carotenoids and retinol.

In future versions of the database, missing and estimated values will be replaced by analytic values as they become available.

In the NCC Food and Nutrient Database there are foods defined as ‘unknown’ (e.g., ‘milk, unknown % fat’). These foods may be selected when a participant does not know the level of detail required for a food.

To assign nutrient values to unknowns NCC uses the nutrient values for the form of the food that is believed to be most commonly consumed in the U.S. For example, the nutrient values for 2% milk are utilized for ‘milk, unknown % fat’. To decide what is most common, NCC relies on scientific and food industry publications that report dietary intake patterns and product sales. Professional judgment is also used where published data is lacking.

If you need to know what food an unknown food defaults to you can look in the output files. The Food File (output file 02) lists the food as it was selected (e.g., milk, unknown % fat). The Component/Ingredient File (output file 01) lists the default food that is associated with the unknown food (e.g., milk, 2 % fat). To quickly identify unknown foods in your dataset use the column in file 2 labeled ‘Unknown (default) Food’. If a food is an unknown there will be a ‘1’ in this column.

The following factors are considered in deciding whether to add a nutrient or other food component to the database:

• Scientific Interest: Is there demand for it? If there is a nutrient or food component you’d like added to the database please let us know(ndsrhelp@umn.edu).
• Availability of Food Composition Information: Is there analytic composition information available for a significant proportion of core foods in the NCC Food and Nutrient Database?
• Quality of Analytic Data: Is the analytic information available of sufficient quality (e.g., obtained using appropriate analytic methods) for use in assigning values to foods in the NCC Food and Nutrient Database?

Brand name products are included if there are significant differences in the nutrient composition of food products within a category. For example, different brands of potato chips are included in the database because the fatty acid content of chips varies notably across brands. Another reason for including brands relates to how people tend to describe the food. For example, commercial cookies tend to be described by brand name (e.g., Oreo® cookie) rather than by generic food description (e.g., chocolate sandwich cookie).

Nutrient values in the NCC Food and Nutrient Database for brand name foods do not precisely match the information on product Nutrition Facts panels for a number of reasons. One reason is that values in the database are not rounded to the nearest whole number as is allowed on the Nutrition Facts panel. Another reason is the database values may not reflect recent changes in the marketplace. For example, if General Mills reformulates Cheerios® today, the nutrient values in the current database may no longer match those on the product label. Discrepancies between database and Nutrition Facts panel values may be due to use of the nutrient composition of representative foods for some brand name product categories for which the nutrient composition across brands is similar. As an example, although the database includes several brands of pretzel twists, the nutrient values assigned to each are based on a representative pretzel twist. It is important to note that use of a representative food is only done when variation in nutrient content across brands of a product is minimal.